Keynote: Driving Innovation with MATLAB and Simulink

Jim Tung will discuss important technology megatrends that are creating both challenges and opportunities in how complex systems are developed and how data is analyzed. He will describe how companies are using models in their design, implementation, and verification activities to leverage these megatrends and drive innovation in product development. He will also look at new capabilities in MathWorks products for data analysis and algorithm development.

Model-Based Design with MATLAB and Simulink – What's New

This presentation covers new developments, features, and capabilities in MATLAB and Simulink for using Model-Based Design for signal processing, communications, and controls applications. You’ll hear about the Embedded MATLAB^® subset for generating embedded C code from MATLAB, PID control for automating control parameter selection, the Simscape™ language for physical system modeling, automatic code generation for FPGAs, and other new features and capabilities.

Mathematical Modeling and Optimization of Wind Turbine Power Generation

This presentation shows how you can use MATLAB and companion toolboxes to model wind turbine power and perform design optimization studies. We use the notebook interface in Symbolic Math Toolbox™ to develop an analytical model of our system, documenting all modeling steps and assumptions. We then integrate the model with MATLAB and calculate optimal design parameters, helping to reduce engineering analysis and prototyping time.

Control System Development with Model-Based Design

In this presentation, we introduce how you can use dynamic simulation to model, design, and verify control systems before testing on hardware. You will learn how Simulink and other MathWorks products can help you reduce development time and costs, improve quality, and deliver systems with higher performance and efficiency. Using a digital motion control case study, we demonstrate how you can:

• Test more thoroughly, by starting before hardware is available and by simulating conditions that would be dangerous or costly to examine with real hardware
• Catch errors early in the development process, when they are easier and cheaper to fix
• Troubleshoot existing design problems systematically and effectively, without tying up actual hardware systems
• Try different control strategies safely and quickly

Design Flows for Next-Generation Signal Processing and Communications Systems

In the development of complex next-generation signal processing and communications products and technologies, design-flow discontinuities are becoming increasingly disruptive and expensive. Shorter design cycles magnify the detrimental impact of these discontinuities. Using examples drawn from a wide range of signal processing and communications systems — including radar, MIMO communications, and image and video processing systems — this presentation describes significant recent advances in modeling and simulation tools and methods that address design-flow discontinuities and fundamentally improve engineering efficiencies in the development process.

For algorithm development, we present System objects for modeling stream processing applications such as audio, video, and other signal processing systems. For system-level modeling and architecture, we present new methods for modeling systems that require dynamic signal dimensions, such as radar, sonar, and phased array applications. New techniques for modeling and simulation of RF systems together with baseband components to perform effective design tradeoffs in Simulink are also presented.

Test and Measurement Application Development with MATLAB

In this presentation, you’ll learn how to build flexible test systems, acquire data from multiple instruments, automate your test routines, and perform live data visualization and analysis, all using a single software environment. We also show how you can use MATLAB to generate automatic test reports, build graphical user interfaces, and share your application as a standalone executable.

Embedded Code Generation and Verification Methodologies

This presentation gives you an overview of MathWorks code generation technology and how you can use it in different phases of your embedded development process. Through an example model with different configuration sets and elaboration, you will learn how to generate code that supports different objectives, such as fixed-point implementation, rapid prototyping, software-in-loop verification, and AUTOSAR. We also look at code generation for a PLC application.

This presentation also introduces model and code verification solutions that can support development according to coding standards such as MISRA C^® or certification standards such as DO-178B and ISO 26262.

Accelerating HDL Code Generation and Verification with Model-Based Design

Teams developing complex applications for implementation on FPGAs and SoCs are under increasing pressure to verify their designs early and find errors before reaching the final implementation stage. In this presentation, we demonstrate MathWorks HDL code generation and verification tools and show how algorithm developers, system designers, and HDL implementation engineers can use these tools to extend Model-Based Design to the FPGA and SoC development process. Abstract simulation models are incrementally refined until automatic HDL code generation with Simulink HDL Coder™ enables the HDL implementation. Functional verification can be performed using EDA Simulator Link™ via test benches developed in high-level MATLAB and Simulink, accelerating the complete HDL development process.

Master Classes

Verification of Highly Reliable Embedded Software (C/C++/Ada) Using Polyspace Products

You are an embedded software engineer responsible for a quality-critical project intended for an application that cannot fail. Deadlines are looming and not enough time has been allocated for coding and testing. Your team has completed code reviews and lots of unit testing, but is it enough? What if a critical defect sneaks through to software deployment and then to production? How can you ensure that your code is absolutely reliable? The use of formal methods–based mathematical techniques may alleviate some of the doubt. Polyspace code verifiers, which implement formal methods–based code verification techniques, can help pinpoint areas of concern in source code. Learn how you can avoid hunting for defects in wide swaths of code and instead use Polyspace code verifiers to know which parts of code will not fail and isolate those aspects of code that will fail or are most likely to fail. In this master class, you’ll learn about practical techniques that you can apply in your embedded software development workflow to deliver high-quality code.

Video and Image Processing Using MATLAB and Simulink

In this master class, you’ll see how to create robust image and video processing solutions as well as computer vision solutions using MATLAB and Simulink. Learn how to access image and video data, perform thorough analyses, develop unique algorithms, visualize intermediate results, optimize performance, and catch design flaws before they become costly to fix. Using examples such as a digital camera pipeline, motion tracking using optical flow, and an implementation of a lane departure warning system, we show how MATLAB, Simulink, and other related tools can help you in various stages of your design, such as image access, image data exploration, algorithm modeling, simulation, and prototyping on an embedded platform (in this case, a TI DM6437 DSP).

Tips and Tricks for Developing Efficient Applications in MATLAB

In this master class, we demonstrate simple ways you can optimize your code to boost execution speed by orders of magnitude. We also address common pitfalls in writing MATLAB code, explore the use of the MATLAB Profiler to find bottlenecks, and introduce the use of Parallel Computing Toolbox™ and MATLAB Distributed Computing Server™ to solve computationally and data-intensive problems on multicore computers and clusters. Demonstrations show how you can apply these techniques to problems that arise in typical applications.
You will learn how to:

• Understand memory usage and vectorization in MATLAB
• Address bottlenecks in your programs
• Optimize file I/O to streamline your code
• Transition from serial to parallel MATLAB programs
• Execute applications on a single multicore or multiprocessor desktop
• Scale from multicore machines to clusters